Career Transition and Feature Engineering

You could argue that résumé writers are also data scientists. We don’t necessarily crunch statistics and use machine learning techniques to draw meaningful conclusions, but we certainly analyze, create, transform, and position data for maximum consumption by a human or technical audience.

One of the fundamental concepts in data science is a process called “feature engineering”. Simply put, it refers to the process of assigning value to different types of raw data, identifying what is most relevant, and removing redundant or irrelevant data. Tell me if this resonates with you:

“For example, let’s consider a retail scenario where a data model is predicting customer churn. Here it might benefit from focusing on features like “purchase frequency” and “customer feedback sentiment”, while ignoring less impactful ones like “the time of day that purchases are made”. This helps to avoid the model getting overwhelmed by noise, improving both its efficiency and accuracy.” ~ Top 11 Data Science Skills in 2025

This analogy is informative for just about any résumé project, magnified by an exponent of 10 in the case of a career change scenario. Like the CNC Machinist who is completing his degree in computer-aided design (CAD) and now wants to pursue job opportunities in that field.

It is easy to get overwhelmed by the noise. Data is frequently the résumé raw material your client wants MORE of. In this case, Jefferson Halliday originally presented a ton of information about his vast experience in CNC and manual milling, lathe setup, and operation. The reader, however, has only a limited attention span to work with. 

So if you replace the feature “job description” with “transferable skills”, add the feature “CAD Projects”, and include the feature “coursework”, you can engineer a targeted product that 1) focuses on the relevancy of his portfolio; 2) eliminates irrelevant skills; and 3) translates the value from his previous experience. When your main sections and sub-sections speak directly to the reader’s attention, you capture the highest and best use of your limited space.

Career changers especially can benefit from the application of feature engineering concepts:

• Data Selection: Résumé professionals are paid to carefully choose relevant information and highlight experiences and skills pertinent to the desired job. Choosing what data to feature is step #1.
• Data Transformation: Sometimes we transform raw data into a more useful format. Often we rephrase or reorganize content to better align with job requirements. Choosing how to feature the data is step #2.
• Emphasis on Quantification: In feature engineering, numerical representations of data can help enhance clarity and impact. Value is paramount; quantifiable value is the holy grail.
• Customization for Specific Goals: Feature engineering is all about customizing and presenting data to showcase the most relevant qualifications and gain a specific performance outcome. The target itself might change, but the feature engineering process changes with it.
• Iterative Refinement: Job seekers often revise their résumés based on feedback or new experiences, just as feature engineers iteratively test and refine features to enhance data model outcomes. Your clients might never be able to do this; luckily, they have you.

The CPRW exam is a feature engineering capability test…so is your next college student, military transition, justice-involved client, or senior-level executive. It’s a good thing you’re a data scientist, prepared to do more than just laundry-list someone’s job history, and smart enough to know that the quantity of data you start with is no match for the quality of data you end up with. 

“The ability to simplify means to eliminate the unnecessary so that the necessary may speak.” ~ Hans Hofmann